AI in the Cosmos

TL;DR

This paper reviews how AI is transforming astrophysics research by enabling efficient data analysis, source classification, and modeling, while addressing challenges like biases and interpretability through Human-Guided AI.

Contribution

It introduces the concept of Human-Guided AI to improve the robustness, interpretability, and ethical application of AI in astrophysics research.

Findings

AI enhances celestial source classification and data modeling.

Generative AI offers new possibilities for astrophysical insights.

Addressing biases and interpretability is crucial for AI's reliable use in astrophysics.

Abstract

Artificial intelligence (AI) is revolutionizing research by enabling the efficient analysis of large datasets and the discovery of hidden patterns. In astrophysics, AI has become essential, transforming the classification of celestial sources, data modeling, and the interpretation of observations. In this review, I highlight examples of AI applications in astrophysics, including source classification, spectral energy distribution modeling, and discuss the advancements achievable through generative AI. However, the use of AI introduces challenges, including biases, errors, and the "black box" nature of AI models, which must be resolved before their application. These issues can be addressed through the concept of Human-Guided AI (HG-AI), which integrates human expertise and domain-specific knowledge into AI applications. This approach aims to ensure that AI is applied in a robust, interpretable, and ethical manner, leading to deeper insights and fostering scientific excellence.